Genetic Programming for Generating Prototypes in Classification Problems
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This document summarizes a research paper that proposes using genetic programming to generate prototypes for classification problems. It describes encoding prototypes as variable-length logical expressions and using a multi-tree representation. The approach evolves a population of these prototypes using genetic operators like crossover and mutation. Classification involves matching samples to prototypes, and fitness is based on recognition rate on a training set. The method was tested on several datasets and achieved higher recognition rates than an alternative genetic programming approach.
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operators we also employ the path prefix strategy as a branch ordering strategy and memory and elitism. Results indicate that there is no significant difference in the performance or the coverage obtained through the two approaches and either could be used in test data generation when coupled with the path prefix strategy, memory and elitism.
On Feature Selection Algorithms and Feature Selection Stability Measures : A ...
On Feature Selection Algorithms and Feature Selection Stability Measures : A ...AIRCC Publishing Corporation
Data mining is indispensable for business organizations for extracting useful information from the huge volume of stored data which can be used in managerial decision making to survive in the competition. Due to the day-to-day advancements in information and communication technology, these data collected from ecommerce and e-governance are mostly high dimensional. Data mining prefers small datasets than high dimensional datasets. Feature selection is an important dimensionality reduction technique. The subsets selected in subsequent iterations by feature selection should be same or similar even in case of small perturbations of the dataset and is called as selection stability. It is recently becomes important topic of research community. The selection stability has been measured by various measures. This paper analyses the selection of the suitable search method and stability measure for the feature selection algorithms and also the influence of the characteristics of the dataset as the choice of the best approach is highly problem dependent.ON FEATURE SELECTION ALGORITHMS AND FEATURE SELECTION STABILITY MEASURES: A C...
Data mining is indispensable for business organizations for extracting useful information from the huge volume of stored data which can be used in managerial decision making to survive in the competition. Due to the day-to-day advancements in information and communication technology, these data collected from ecommerce and e-governance are mostly high dimensional. Data mining prefers small datasets than high dimensional datasets. Feature selection is an important dimensionality reduction technique. The subsets selected in subsequent iterations by feature selection should be same or similar even in case of small perturbations of the dataset and is called as selection stability. It is recently becomes important topic of research community. The selection stability has been measured by various measures. This paper analyses the selection of the suitable search method and stability measure for the feature selection algorithms and also the influence of the characteristics of the dataset as the choice of the best approach is highly problem dependent.
On Feature Selection Algorithms and Feature Selection Stability Measures : A...
On Feature Selection Algorithms and Feature Selection Stability Measures : A...AIRCC Publishing Corporation
Data mining is indispensable for business organizations for extracting useful information from the huge
volume of stored data which can be used in managerial decision making to survive in the competition. Due
to the day-to-day advancements in information and communication technology, these data collected from e-
commerce and e-governance are mostly high dimensional. Data mining prefers small datasets than high
dimensional datasets. Feature selection is an important dimensionality reduction technique. The subsets
selected in subsequent iterations by feature selection should be same or similar even in case of small
perturbations of the dataset and is called as selection stability. It is recently becomes important topic of
research community. The selection stability has been measured by various measures. This paper analyses
the selection of the suitable search method and stability measure for the feature selection algorithms and
also the influence of the characteristics of the dataset as the choice of the best approach is highly problem
dependent.Optimal rule set generation using pso algorithm
Classification and Prediction is an important resea
rch area of data mining. Construction of
classifier model for any decision system is an impo
rtant job for many data mining applications.
The objective of developing such a classifier is to
classify unlabeled dataset into classes. Here
we have applied a discrete Particle Swarm Optimizat
ion (PSO) algorithm for selecting optimal
classification rule sets from huge number of rules
possibly exist in a dataset. In the proposed
DPSO algorithm, decision matrix approach was used f
or generation of initial possible
classification rules from a dataset. Then the propo
sed algorithm discovers important or
significant rules from all possible classification
rules without sacrificing predictive accuracy.
The proposed algorithm deals with discrete valued d
ata, and its initial population of candidate
solutions contains particles of different sizes. Th
e experiment has been done on the task of
optimal rule selection in the data sets collected f
rom UCI repository. Experimental results show
that the proposed algorithm can automatically evolv
e on average the small number of
conditions per rule and a few rules per rule set, a
nd achieved better classification performance
of predictive accuracy for few classes.
Similarity Features, and their Role in Concept Alignment Learning
The document describes a study that used three classifiers - Markov Random Field, Multi-objective Evolution Strategy, and Support Vector Machine - to classify concept mappings between two thesauri based on instance similarity. The classifiers were able to achieve a performance of around 90% accuracy. Analysis of the classifiers found some expected important features like label similarity and subject, but also surprisingly important features like date. The study demonstrated that machine learning can be effective for the instance-based mapping task and help identify meaningful similarity features.
Similar to Genetic Programming for Generating Prototypes in Classification Problems (20)
Efficiently finding the parameter for emergign pattern based classifier
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Survey and Evaluation of Methods for Tissue Classification
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Enhanced Genetic Algorithm with K-Means for the Clustering Problem
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On Feature Selection Algorithms and Feature Selection Stability Measures : A ...
On Feature Selection Algorithms and Feature Selection Stability Measures : A ...
ON FEATURE SELECTION ALGORITHMS AND FEATURE SELECTION STABILITY MEASURES: A C...
ON FEATURE SELECTION ALGORITHMS AND FEATURE SELECTION STABILITY MEASURES: A C...
On Feature Selection Algorithms and Feature Selection Stability Measures : A...
On Feature Selection Algorithms and Feature Selection Stability Measures : A...
Similarity Features, and their Role in Concept Alignment Learning
Similarity Features, and their Role in Concept Alignment Learning
Genetic Programming for Generating Prototypes in Classification Problems
- 1. GENETIC PROGRAMMING FOR GENERATING PROTOTYPES IN CLASSIFICATION PROBLEMS Presented by: Tarundeep Dhot Dept of ECE Concordia University
- 2. This presentation is based on a research paper written by the following authors: L.P. Cordella, C. De Stefano, F. Fontanella, A Marcelli This paper was published at: The 2005 IEEE Congress on Evolutionary Computation This presentation is solely meant for educational purposes. Acknowledgements
- 13. LEARNING CLASSIFICATION RULES (cont:) GENETIC OPERATORS: Crossover and mutation are used as the genetic operator. Crossover: Applied to two chromosomes C 1 and C 2 and yields two new chromosomes by swapping the lists of the initial chromosomes. Fig: (a) and (b) Chromosome C 1 and C 2 of length 4 and 3. (c) and (d) Chromosomes obtained after crossover at t 1 = 2 and t 2 = 1
- 14. LEARNING CLASSIFICATION RULES (cont:) GENETIC OPERATORS: Mutation: It is independently applied to every tree of the chromosome C with probability p m . More specifically, mutation operator is applied by randomly choosing a single non-terminal node T i in a given tree T s . If there are n non-terminal nodes in a chromosome C, probability of mutating each single node of C = p m / n.
- 15. EXPERIMENTAL RESULTS The proposed approach was tested on three standard databases (IRIS, BUPA and Vehicle available on the UCI website and also compared another GP based approach. The method showed better results with higher recognition rates. Table: Average Recognition rates (%). R 1 proposed classifer R 2 comparison classifier. Data Sets R 2 R 1 IRIS 98.67 99.4 BUPA 69.87 74.3 Vehicle 61.75 66.5
- 17. THANK YOU !!